Time and Place
- Time: Dec 17 at 16.30 - 19.30 in AS2 / TUAS (check Oodi for possible updates)
- There will be another (final) chance in late February. Follow Oodi for details.
Only pens and simple calculators (nelilaskin) or function calculators (funktiolaskin) allowed. Books, notes, phones, computers, graphical calculators etc. are not permitted.
Maximum number of points is 50. The minimum required for passing the course is 25. Grading emphasizes conceptual understanding over mathematical precision. Please note that equivocated answers -- i.e., fishing points by generating answers that are blatantly false or might in real life have damaging consequences -- will be penalized by deducing points.
Contents and learning objectives
The slides and assignments marked as Recommended are the primary material for the exam and sufficient for a passing grade. To aim for highest grades, we advise reading the additional papers listed in Materials.
Lecture 1: Combinatorial optimization: 1) Understanding of uses and assumptions of computational interaction and design; 2) Ability to cast simple design problems as combinatorial optimization tasks, including design space, objectives, constraints.
Lecture 2: Perception and attention: 1) Windows of visibility; 2) Rosenholtz' clutter model; 3) Ability to predict how bottom-up (saliency) and top-down attention would proceed for a given layout.
Lecture 3: Control: 1) Ability to predict movement with Fitts' law and steering law when parameters are given; 2) Ability to model (block diagram) a pointing gesture using control theory, in particular a block diagram implementing 2OL or similar model.
Lectures 4 and 5: Input: 1) Ability to tell what kinds of filtering are needed for different issues in raw sensor data; 2) Understanding of operating principles of a filter (e.g., 1€ filter) and a recognizer (e.g., 1$ recognizer). 3) Ability to construct a decoder for single or sequential input.
Lecture 6: Bayesian human-in-the-loop optimization: Understanding of core concepts in Bayesian optimization, including surrogate model, prior, update, acquisition function.
Lecture 7: Integer programming: Ability to formulate a menu and keyboard design problem as a mixed integer linear program.
Lecture 8: Biomechanics: Ability to evaluate the fatiguability of a given posture or movement using the Consumed Endurance model (when parameter values given).
Lecture 9: Formal methods: 1) Ability to draw a finite state diagram for simple interactive devices; 2) Ability to interpret a simple verification statement expressed with temporal logic (see slides).
Lecture 10: Cognitive models: Ability to formulate an information foraging diagram (patch model) for a given application case.
Lecture 11: Bandits: 1) Understanding of the bandit problem; 2) Understanding of how exploration/exploitation is solved; 3) Understanding of contextual bandits.
Lectures 12-13: Reinforcement learning: 1) Ability to formulate a navigation or decision-making task in interaction as a reinforcement learning problem, including the Markov decision process (MDP). 2) Understanding of difference between POMDP and MDP.
Format and task types
The exam will consist of 10 pages. Each page will contain one task worth of max. 5 points. The following task types may be used to test general understanding:
- Definition: E.g., define a concept in text or by a diagram.
- Explanation: E.g., explain a concept, model, or theory briefly in text or by a diagram.
- Assessment of a theory or model: E.g,. analyze pros and cons of a given theory, model, or concept.
- Short essay: E.g., provide an account of some phenomenon in interaction from a perspective coming from the course materials.
The following task types may be used to test the ability to apply knowledge to practical problems. In these problems:
- Analysis: E.g., given a design, analyze its different aspects from the perspective of a concept, model, or theory.
- Comparison: E.g., given two designs, analyze their pros and cons from the perspective of a concept, model, or theory.
- Numerical problem: E.g., given a design, identify the value of some property or outcome using a model.
- Re-design: E.g., given a design, propose a simple improvement by reference to a concept, theory, or model.
- Assessment of a design: E.g., given a design, analyze its pros and cons using appropriate models, concepts, or theories provided in the course. Assessment can be verbal or numerical.